1. Field
De-interlacing of interlaced fields of digital video.
2. Background
Compressed or coded digital video is quickly becoming ubiquitous for video storage and communication. Generally speaking, video sequences contain a significant amount of statistical and subjective redundancy within and between frames. Thus, video compression and source coding provides the bit-rate reduction for storage and transmission of digital video data by exploiting both statistical and subjective redundancies, and to encode a “reduced set” of information using entropy coding techniques. This usually results in a compression of the coded video data compared to the original source data. The performance of video compression techniques depends on the amount of redundancy contained in the image data as well as on the actual compression techniques used for coding. For example, video compression or coding algorithms are being used to compress digital video for a wide variety of applications, including video delivery over the Internet, digital television (TV) broadcasting, satellite digital television, digital video disks (DVD), DVD players, set top boxes, TV enabled personal computers (PC), as well as video storage and editing.
The performance of modern compression algorithms, such as moving picture experts group (MPEG) (e.g., such as MPEG2 (ISO/IEC 13818-2:2000, published 2000) or MPEG4 (ISO/IEC 14496-3:2000, published 2004)), can often reduce raw video data rates by factors of 15 to 80 times without considerable loss in reconstructed video quality. The basic statistical property upon which MPEG compression techniques rely is inter-pel correlation, including the assumption of simple correlation translatory motion between consecutive frames. Since video sequences usually contain statistical redundancies in both temporal and spatial directions, it is assumed that the magnitude of a particular image pel can be predicted from nearby pixels within the same frame (using intra-frame coding techniques) or from pixels of a nearby frame (using inter-frame techniques). It is clear that in some circumstances, such as during scene changes of a video sequence, the temporal correlation between pixels and nearby frames is small or even disappears (e.g., the video scene is then an assembly over time of uncorrelated still images). In such cases, intra-frame coding techniques are appropriate to explore spatial correlation to achieve sufficient data compression.
MPEG compression algorithms employ discrete cosine transform (DCT) coding techniques on image blocks of 8×8 pixels to effectively explore spatial correlations between nearby pixels within the same image. However, if the correlation between pixels in nearby frames is high, such as where two consecutive frames have similar or identical content, it is desirable to use inter-frame coding techniques employing temporal prediction, such as motion compensated prediction between frames. MPEG video coding schemes use an adaptive combination of both temporal motion compensated prediction followed by a transform coding of the remaining spatial information to achieve high data compression. For example, digital video is often compressed in 4×4 or 8×8 blocks of pixels using motion-compensated (MC) prediction combined with the DCT block transform. The video encoder codes the prediction coefficients (motion vectors, frame field, MC decision, direction, etc.) as well as the DCT coefficients into the compressed bit-stream. The decoder then uses these parameters to decode and reconstruct the video. For example, the DCT coefficients, MC mode, and motion vectors may be used to reconstruct pixels to form decoded fields of interlaced video data. The reconstructed video fields are then passed through a de-interlacer to form frames, when the decoded video is to be displayed by a non-interlace display technology.